Quickstart

It is a part of the Recommandation System called Execution Simulator. The big picture of the Recommandation System is shown below.

A package that simulates I/O application behavior in multi-tiered HPC system.

Features

• Encodes a simple representation of an application as a sequence of I/O dominant and compute dominant phases.

• Storage tiers have their own performances and capacities that are shared resources between phases and applications.

• Support ephemeral tiers that lasts only during the application runtime.

Examples

Simple sequential application

initial imports

import simpy
import time
from cluster_simulator.cluster import Cluster, Tier, bandwidth_share_model, compute_share_model, get_tier, convert_size
from cluster_simulator.phase import DelayPhase, ComputePhase, IOPhase
from cluster_simulator.application import Application
from cluster_simulator.analytics import display_run

application formalism

# preparing execution environment variables
env = simpy.Environment()
data = simpy.Store(env)
# app1 : read 3GB -> compute 15 seconds duration for 1 -> write 7GB
app1 = Application( env, name="app1", # name of the app in the display
                    compute=[0, 15],  # two events, first at 0 and second at 15, and compute between them
                    read=[3e9, 0],    # read 3GB at 0, before compute phase, at the end do nothing (0)
                    write=[0, 5e9],  # write 5GB at first event, and 10GB at the second, after compute phase
                    data=data)        # collected data for monitoring

preparing the cluster compute and storage tiers facilities

ssd_bandwidth =   {'read':  {'seq': 210, 'rand': 190}, # throughput for read ops in MB/s
                   'write': {'seq': 100, 'rand': 100}} # for read/write random/sequential I/O

# register the tier with a name and a capacity
ssd_tier = Tier(env, 'SSD', bandwidth=ssd_bandwidth, capacity=200e9)
hdd_bandwidth = {'read':  {'seq': 80, 'rand': 80},
                 'write': {'seq': 40, 'rand': 40}}

# register the tier with a name and a capacity
hdd_tier = Tier(self.env, 'HDD', bandwidth=hdd_bandwidth, capacity=1e12)

# register the cluster by completing the compute characteristics
cluster = Cluster(env, compute_nodes=3,     # number of physical nodes
                       cores_per_node=2,    # available cores per node
                       tiers=[hdd_tier, ssd_tier]) # associate storage tiers to the cluster

running the application and get traces

# placement list indicated where each I/O run in the tiers hierarchy
env.process(app1.run(cluster, placement=[0, 1])) # run I/O n°1 on first tier (HDD), the second on SSD
env.run()

We get the following (interactive) timeseries plot. The application lasts 102.5 seconds. The first read I/O conveys 5GB of data from the HDD tier at a rate of 80MB/s. Its duration is 37.5 seconds. Then it is followed by a compute dominant phase of 15 seconds. Finally the write I/O phase happens on the SSD tier in 50 seconds at a 100MB/s rate.

Two parallel applications

Now we define two similar applications that will run on the same cluster and place their I/O operations on the same SSD tier.

running concurrent applications on the same cluster

app1 = Application(env, name="app1", read=[4e9, 0], compute=[0, 15],  write=[0, 10e9],
               data=data)
app2 = Application(env, name="app2", read=[7e9, 0], compute=[0, 10],  write=[0, 3e9],
               data=data)

env.process(app1.run(cluster, placement=[1, 1])) # both I/O are placed in SSD
env.process(app2.run(cluster, placement=[1, 1])) # app2 I/O are also in SSD
env.run()

The two apps share equally an available bandwidth of 210MB/s for reading from SSD. Once app1 finishes the I/O reading at t = 38.09 seconds, it frees the bandwidth for the first reading I/O of the application 2. Hence the throughput reaches 210MB/s between 38.09 and 48.09 seconds. After this interval, the writing I/O of app1 starts while the first reading I/O still not finished, so they will share again available bandwidth.

• TODO